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Record W2488012951 · doi:10.1158/1538-7445.am2016-3718

Abstract 3718: Sensitization of human tumor cells to chemotherapy drugs by antisense downregulation of RAD51: Targeting DNA repair to induce synthetic lethality

2016· article· en· W2488012951 on OpenAlexaboutno aff
Peter J. Ferguson, Mateusz Rytelewski, Mark Vincent, James Koropatnick

Bibliographic record

VenueCancer Research · 2016
Typearticle
Languageen
FieldMedicine
TopicPARP inhibition in cancer therapy
Canadian institutionsnot available
Fundersnot available
KeywordsOlaparibSynthetic lethalityRAD51Cancer researchDNA repairCisplatinBiologyDU145DNA damagePoly ADP ribose polymerasePARP inhibitorGene knockdownHomologous recombinationLNCaPCancer cellMolecular biologyCell cultureCancerDNAPolymeraseChemotherapyGenetics

Abstract

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Abstract The inherent genomic instability of cancer cells has been exploited as a tumor-selective drug target to treat tumors that are deficient in specific mechanisms of DNA repair. Inhibitors of poly(ADP-ribose) polymerase (PARP) are routinely used clinically against tumors deficient in BRCA1 or BRCA2, due to the poor capacity of these cells to undergo homologous recombination repair (HRR). This exploitation of a tumor cell deficiency to enhance selectivity to a particular drug is “synthetic lethality” (Nature 434: 913, 2005). To make use of this phenomenon in tumors that may not be inherently hypersensitive to a particular treatment, we have sought to induce synthetic lethality by down-regulating essential components of DNA repair, in particular BRCA2, to sensitize cells to chemotherapy drugs [Mol Oncol 8(8): 1429-1440, 2014]. Given that an important function of BRCA2 is to modulate the action of RAD51 in HRR, we determined whether antisense knockdown of RAD51 could enhance tumor cell sensitivity to the PARP inhibitor olaparib and the DNA-crosslinking agent cisplatin. Four different anti-RAD51 siRNA molecules (Dharmacon), targeting coding sequences, were tested against cell lines representative of different tumor types in an in vitro assay of proliferation (non-small cell lung cancer line A549b, colon carcinoma line HT-29, and prostate carcinoma lines DU145 and LNCaP). The siRNAs, as single agents, inhibited proliferation in a concentration-dependent fashion and to varying degrees, and sensitized tumor cells to olaparib. A sequence that targeted region 1169-1187 of the RAD51 cDNA (NM_002875.4) was utilized for further studies. At concentrations of anti-RAD51 siRNA 51a that inhibited proliferation of cell lines by less than 50%, 51a enhanced cytotoxicity of olaparib by over 90% and of cisplatin by 60-90%. In all cell lines except LNCaP (with mutant BRCA2) the combination of siRNAs against RAD51 and BRCA2 acted cooperatively to enhance cytotoxicity of olaparib and cisplatin. Notably, when used together, each siRNA down-regulated expression of its respective target mRNA, as demonstrated by quantitative RT-PCR, without interfering with the activity of the other. RAD51 can be exploited clinically as a target for inherent or induced synthetic lethality to DNA-damaging agents (e.g., cisplatin) or inhibitors of DNA repair (e.g., olaparib). Such treatment can include tumors with BRCA2-deficiency, either inherent or induced, to yield at least an additive anticancer effect. MR is a scholar of the CIHR Strategic Training Program in Cancer Research and Technology Transfer (CaRTT) and a recipient of the CIHR Banting and Best Canada Graduate Scholarship. Citation Format: Peter J. Ferguson, Mateusz Rytelewski, Mark D. Vincent, James Koropatnick. Sensitization of human tumor cells to chemotherapy drugs by antisense downregulation of RAD51: Targeting DNA repair to induce synthetic lethality. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3718.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.051
GPT teacher head0.405
Teacher spread0.354 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2016
Admission routes1
Has abstractyes

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